Enterprises typically process unstructured information stored in a number of data sources, such as documents, file systems, databases, corpus, and the like. As used herein, “unstructured information” may refer to data that lacks structure that provides a logical, conceptual, or semantic meaning for the data, as may be used for a particular service. For example, a textual document that includes the phrase “cup holder” and the phrase “car cup holder” may lack data that indicates that these two terms actually refer to the same concept of a car cup holder.
In a typical text analytic system, a dictionary that maps terms to concepts may be used to generate analytics that provides structure to the unstructured information. For example, a dictionary may allow an end-user to specify things like “cup holder” and “car cup holder” as the same entity. To broaden the dictionary to cover many terms used in an industry or domain, the end-user may specify mappings for a significant number of terms. Further, the end-user may also spend significant time in maintaining the dictionary or porting the dictionary to other domains.